Combined sensing methods for cognitive radio

ABSTRACT

Embodiments of cognitive radio technology can recover and utilize under-utilized portions of statically-allocated radio-frequency spectrum. A plurality of sensing methods can be employed. Transmission power control can be responsive to adjacent channel measurements. Digital pre-distortion techniques can enhance performance. Embodiments of a high DNR transceiver architecture can be employed.

PRIORITY

This application is related to and claims priority under 35 U.S.C.119(e) to U.S. Provisional Patent Application No. 60/890,801 filed onFeb. 20, 2007 entitled “SYSTEM AND METHOD FOR COGNITIVE RADIO” by HaiyunTang the complete content of which is hereby incorporated by reference.

BACKGROUND

1. Field of the Invention

The inventions herein described relate to systems and methods forcognitive radio.

2. Description of the Related Art

Spectrum Utilization Problems

1A recent study by the FCC Spectrum Task Force [United States' FederalCommunications Commission (FCC), “Report of the spectrum efficiencyworking group,” November 2002,http://www.fcc.gov/sptf/files/IPWGFinalReport.pdf] found that while theavailable spectrum becomes increasingly scarce, the assigned spectrum issignificantly underutilized. This imbalance between spectrum scarcityand spectrum underutilization is especially inappropriate in thisInformation Age, when a significant amount of spectrum is needed toprovide ubiquitous wireless broadband connectivity, which isincreasingly becoming an indispensable part of everyday life.

Static spectrum allocation over time can also result in spectrumfragmentation. With lack of an overall plan, spectrum allocations in theUS and other countries over the past several decades can appear to berandom. Despite some efforts to serve best interests at the time, thisleads to significant spectrum fragmentation over time. The problem isexacerbated at a global level due to a lack of coordinated regionalspectrum assignments. In order to operate under such spectrumconditions, a device can benefit from operational flexibility infrequency and/or band shape; such properties can help to maximallyexploit local spectrum availability.

To address the above problems, an improved radio technology is neededthat is capable of dynamically sensing and locating unused spectrumsegments, and, communicating using these spectrum segments whileessentially not causing harmful interference to designated users of thespectrum. Such a radio is generally referred to as a cognitive radio,although strictly speaking, it may perform only spectrum cognitionfunctions and therefore can be a subtype of a broad-sense cognitiveradio [J. M. III, “Cognitive radio for flexible mobile multimediacommunications,” Mobile Networks and Applications, vol. 6, September2001.] that learns and reacts to its operating environment. Key aspectsof a cognitive radio can include: Sensing: a capability to identify usedand/or unused segments of spectrum. Flexibility: a capability to changeoperating frequency and/or band shape; this can be employed to fit intounused spectrum segments.

Non-Interference: a Capability to Avoid Causing Harmful Interference toDesignated Users of the Spectrum.

Such a cognitive radio technology can improve spectrum efficiency bydynamically exploiting underutilized spectrum, and, can operate at anygeographic region without prior knowledge about local spectrumassignments. It has been an active research area recently.

FCC Spectrum Reform Initiatives

FCC has been at the forefront of promoting new spectrum sharingtechnologies. In April 2002, the FCC issued an amendment to Part 15rules that allows ultra-wideband (UWB) underlay in the existing spectrum[FCC, “FCC first report and order: Revision of part 15 of thecommission's rules regarding ultra-wideband transmission systems,” ETDocket No. 98-153, April 2002]. In June 2002, the FCC established aSpectrum Policy Task Force (SPTF) whose study on the current spectrumusage concluded that “many portions of the radio spectrum are not in usefor significant periods of time, and that spectrum use of these ‘whitespaces’ (both temporal and geographic) can be increased significantly”.SPTF recommended policy changes to facilitate “opportunistic or dynamicuse of existing bands.” In December 2003, FCC issued the notice ofproposed rule making on “Facilitating Opportunities for Flexible,Efficient and Reliable Spectrum Use Employing Cognitive RadioTechnologies” [FCC, “Facilitating opportunities for flexible, efficient,and reliable spectrum use employing cognitive radio technologies,” ETDocket No. 03-108, December 2003] stating that “by initiating thisproceeding, we recognize the importance of new cognitive radiotechnologies, which are likely to become more prevalent over the nextfew years and which hold tremendous promise in helping to facilitatemore effective and efficient access to spectrum.”

While both UWB and cognitive radio are considered as spectrum sharingtechnologies, their approaches to spectrum sharing are substantiallydifferent. UWB is an underlay (below noise floor) spectrum sharingtechnology, while cognitive radio is an overlay (above noise floor) andinterlay (between primary user signals) spectrum sharing technology asshown in FIG. 1. Through sensing combined with operational flexibility,a cognitive radio can identify and make use of spectral “white spaces”between primary user signals. Because a cognitive user signal resides insuch “white spaces”, high signal transmission power can be permitted aslong as signal power leakage into primary user bands does not embodyharmful interference.

Broadcast TV Bands

Exemplary broadcast TV bands are shown in Graph 200 of FIG. 2. Each TVchannel is 6 MHz wide. Between 0 and 800 MHz, there are a total of 67 TVchannels (Channels 2 to 69 excluding Channel 37 which is reserved forradio astronomy). The NPRM [FCC, May 2004, op. cit.] excludes certainchannels for unlicensed use: Channels 2-4, which are used by TVperipheral devices, and Channels 52-69, which are considered for futureauction. Among the channels remaining, Channels 5-6, 7-13, 21-36, and38-51 are available for unlicensed use in all areas. Unlicensed use inChannels 14-20 is allowed only in areas where they are not used bypublic safety agencies [FCC, May 2004, op. cit.].

It can be appreciated that Channels 52-69 are currently used by TVbroadcasters and it is not clear if/when they will be vacated. There issignificant interference in the lower channels 5-6 and 7-13. Based onthese considerations, the spectrum segment 470-806 MHz covering TVchannels 14-69 can be of particular interest.

Spectrum Opportunity in the TV Bands

Spectrum opportunity can be a direct result of incumbent systeminefficiency. In TV bands, a signal from a TV tower can cover an areawith a radius of tens of kilometers. TV receivers can be sensitive tointerference such that TV cell planning may be very conservative toensure there is essentially no co-channel interference. This can leave asubstantial amount of “white spaces” between co-channel TV cells asillustrated in the the Map 300 of FIG. 3. Those “white spaces” canconstitute an opportunistic region for cognitive users on a particularTV channel. Each TV channel may have a differently shaped opportunisticregion. The total spectrum opportunity at any location can comprise thetotal number of opportunistic regions covering the location. Ameasurement in one locality shows an average spectrum opportunity in TVchannels 14-69 of about 28 channels; that can be expressed as anequivalent bandwidth of approximately 170 MHz.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 graph of spectrum sharing technologies: UWB and cognitive radio

FIG. 2 graph of exemplary television channel bands

FIG. 3 map of television co-channel coverage areas and opportunisticregion

FIG. 4 diagram of a cognitive radio system

FIG. 5 DTV signal Data Field Sync segment

FIG. 6 NTSC signal video (V), chroma (C), and audio (A) carriers

FIG. 7 graph: Simulated NTSC signal sensing gain contours

FIG. 8 diagram: false detection probability and loss detectionprobability.

FIG. 9 graph: Sensing error floor contours

FIG. 10 diagram: hidden terminal problem.

FIG. 11 diagram: relations between various signal power leveldefinitions.

FIG. 12 graph: PD/PS and P0/P1 for interference-free operation

FIG. 13 diagram: hidden terminal problem in shadowing environment.

FIG. 14 graph: Hidden terminal probability with sensing informationsharing

FIG. 15 graph: Critical mass for a specified HTP in a shadowingenvironment.

FIG. 16 radio signal coverage areas at different data rates.

FIG. 17 ATSC Data Frame format

FIG. 18 diagram: ATSC signal sensing

FIG. 19 diagram: auto-correlation processing.

FIG. 20 diagram: NTSC signal sensing

FIG. 21 diagram: Energy sensing

FIG. 22 diagram: Combined sensing methods

DETAILED DESCRIPTION

FIG. 4 depicts an embodiment of a cognitive radio system in blockdiagram. A transceiver 401 can be coupled to and/or in communicationwith one or more antennae 402. Baseband signal processing can beprovided by elements of a baseband processor 403. Elements of a basebandprocessor 403 can comprise a sensing processor 404, a transmit powercontrol element 405, and a pre-distortion element 406. In someembodiments a pre-distortion element 406 can be coupled to and/or incommunication with a transceiver 401. In some embodiments a transmitpower control element can be coupled to and/or in communication with atransceiver 401. In some embodiments a collective sensing element 407can be coupled to and/or in communication with a baseband processor 403and/or elements comprising a baseband processor.

In some embodiments transceiver 401 can comprise transceiver and/ortransmitter and/or receiver mechanisms disclosed herein. In someembodiments sensing element 404 can comprise one or more sensingmechanisms as described herein. By way of example and not limitationthese sensing mechanisms can include energy sensing, NTSC signalsensing, and/or ATSC signal sensing. In some embodiments a collectivesensing element 407 can provide collective sensing mechanisms asdescribed herein.

In some embodiments transmit power control 405 can support adaptivetransmit power control mechanisms described herein. In some embodimentspre-distortion element 406 can provide digital pre-distortion mechanismsas described herein.

In some embodiments baseband processor 403 can support additionalprocessing mechanisms as described herein. By way of example and notlimitation these mechanisms can include filtering and/or digital mixing.

Sensing for TV Band Adaptive Spectrum System

Sensing methods can generally be separated into two categories: 1)energy sensing and 2) phase sensing. Energy sensing can measure spectrumenergy of a target signal and can be a fundamental sensing method whennothing else is known about the target signal. In some embodiments, areceived signal can be transformed to a frequency domain in order toperform an energy sensing operation. Some embodiments can employ an FFTand/or any other known and/or convenient transformation to a frequencydomain. Signal energies in specified frequency regions can subsequentlybe measured.

In some embodiments and/or circumstances phase sensing can achievebetter performance than energy sensing. Phase sensing can require that atarget signal contains one or more known signal patterns. Phase sensingcan be achieved by correlating a received signal with one or more knownpatterns. In some embodiments of a TV-band cognitive radio system, theincumbent signal can contain known signal patterns. In some embodimentsan incumbent signal can be a DTV signal and/or an NTSC signal and/or anyother known and/or convenient signal organized as a channel and/orhaving adjacent channels. In some embodiments specified performancelevels can be achieved through the use of phase sensing techniques. Theterms phase sensing, waveform sensing, and/or signal sensing can beconsidered substantially identical as discussed herein.

DTV Signal Sensing:

A broadcast DTV signal can contain a Data Field Sync [ATSC, “Digitaltelevision standard,” ATSC Digital Television Standard, August 2001]segment of 832 symbols (at a symbol period 0.186 μs and/or a samplingfrequency of 5.38 MHz) occurring every 24.2 ms. The diagram 500 of FIG.5 shows the format of a DTV Data Field Sync segment that contains a511-element PN sequence and three 63-element PN sequences, all of whichcan be specified and/or known.

A PN sequence can have useful correlation properties. A PN sequencecorrelated with any rotated version of itself can produce a result of−1, except when two PN sequences are aligned, in which case the resultis equal to the PN sequence length. A PN sequence can be cyclicallyextended to create an infinite sequence s(n). It follows that

$\begin{matrix}{{\sum\limits_{n = 0}^{N_{c} - 1}{s*(n){s\left( {n - m} \right)}}} = {{\left( {N_{c} + 1} \right){\delta (m)}} - 1}} & (1)\end{matrix}$

under the assumption m∈(−N_(c), N_(c)). The above correlation can benormalized to give

$\begin{matrix}{{\frac{1}{N_{c}}{\sum\limits_{n = 0}^{N_{c} - 1}{s*(n){s\left( {n - m} \right)}}}} = {{{\frac{\left( {N_{c} + 1} \right)}{N_{c}}{\delta (m)}} - \frac{1}{N_{c}}} \approx {\delta (m)}}} & (2)\end{matrix}$

where the approximation can be useful for conditions where N_(c)>>1. Asensing metric M(m) can be generated by correlating a received signalwith a known PN sequence

$\begin{matrix}{{M(m)} = {\underset{\underset{{DTV}\mspace{14mu} {sensing}\mspace{14mu} {operator}}{}}{\frac{1}{N_{c}}{\sum\limits_{n = 0}^{N_{c} - 1}{s*\left( {n - m} \right)}}}{y(n)}}} & (3)\end{matrix}$

which can be expanded as

$\begin{matrix}\begin{matrix}{{M(m)} = {\frac{1}{N_{c}}{\sum\limits_{n = 0}^{N_{c} - 1}{s*{\left( {n - m} \right)\left\lbrack {{\sum\limits_{p = {- \infty}}^{\infty}{{c(p)}{s\left( {n - p} \right)}}} + {z(n)}} \right\rbrack}}}}} \\{= {{\sum\limits_{p = {- \infty}}^{\infty}{{c(p)}{\sum\limits_{n = 0}^{N_{c} - 1}{s*\left( {n - m} \right){s\left( {n - p} \right)}}}}} +}} \\{{\frac{1}{N_{c}}{\sum\limits_{n = 0}^{N_{c} - 1}{s*\left( {n - m} \right){z(n)}}}}} \\{= {{\sum\limits_{p = {- \infty}}^{\infty}{{c(p)}{\delta \left( {p - m} \right)}}} + {Z_{M}(m)}}} \\{= {{c(m)} + {Z_{M}(m)}}}\end{matrix} & (4)\end{matrix}$

where the channel tap coefficient c(m) can be assumed to be nonzero for(only) a few taps around m=0 and where

$\begin{matrix}\begin{matrix}{{E\left\lbrack {{Z_{M}(m)}{Z_{M}^{*}(n)}} \right\rbrack} = {E\left\lbrack {\frac{1}{N_{c}}{\sum\limits_{p = 0}^{N_{c} - 1}{{s^{*}\left( {p - m} \right)}{z(p)}\frac{1}{N_{c}}}}} \right.}} \\\left. {\sum\limits_{q = 0}^{N_{c} - 1}{{s\left( {q - n} \right)}{z^{*}(q)}}} \right\rbrack \\{= {\frac{1}{N_{c}}{\sum\limits_{p = 0}^{N_{c} - 1}{{s^{*}\left( {p - m} \right)}\frac{1}{N_{c}}{\sum\limits_{q = 0}^{N_{c} - 1}{s\left( {q - n} \right)}}}}}} \\{{\sigma^{2}{\delta \left( {p - q} \right)}}} \\{= {\frac{\sigma^{2}}{N_{c}^{2}}{\sum\limits_{p = 0}^{N_{c} - 1}{{s^{*}\left( {p - m} \right)}{s\left( {p - n} \right)}}}}} \\{= {\frac{\sigma^{2}}{N_{c}}{\delta \left( {n - m} \right)}}}\end{matrix} & (5)\end{matrix}$

Notably, Z_(M)(m)s can be independent Gaussian random variables withpower

$\frac{\sigma^{2}}{N_{c}}.$

A symbol signal-to-noise-ratio SNR can be expressed

$\begin{matrix}{{SNR} = \frac{\sum\limits_{n = {- \infty}}^{\infty}{{c(n)}}^{2}}{\sigma^{2}}} & (6)\end{matrix}$

A sensing metric M(m) (4) can then have a SNR

$\begin{matrix}{{SNR}_{M} = {\frac{{{c(m)}}^{2}}{E\left\lbrack {{Z_{M}(m)}}^{2} \right\rbrack} = {N_{c} \cdot {SNR} \cdot \frac{{{c(m)}}^{2}}{\sum\limits_{n = {- \infty}}^{\infty}{{c(n)}}^{2}}}}} & (7)\end{matrix}$

Rake combining can be employed to eliminate the factor

$\begin{matrix}\frac{{{c(m)}}^{2}}{\sum\limits_{n = {- \infty}}^{\infty}{{c(n)}}^{2}} & (8)\end{matrix}$

in equation (7). A sensing gain over a nominal symbol SNR can then beN_(c). For example, a 63-element PN sequence can be used for sensing,wherein a sensing gain can be calculated

10 log₁₀(63)=18 dB   (9)

In some embodiments, a sensing gain of more than 20 dB can be achievedwhen a 511-element PN sequence is employed.

ATSC Signal Sensing: Signal Sensing:

The format of the Data Field Sync segment (one in each Data Field of 313832-symbol segments) can be as shown in diagram 500 of FIG. 5. The DataField Sync segment contains 4 binary-modulated pseudo-random sequence,i.e. one PN511 and 3 PN63. These are intended for ATSC receiver channelequalization.

When coherently combined, the PN sequences can provide significantcoding gain over a nominal symbol SNR and can be employed for someembodiments with reliable low-threshold ATSC signal detection. Forexample, a PN63 sequence can provide about 10*log10(63)=18 dB gain overnominal SNR and a PN511 sequence can provide about 10*log10(511)=27 dBgain over a nominal SNR.

Diagram 1800 of FIG. 18 depicts an embodiment of a detailed blockdiagram for an ATSC signal sensing algorithm for an input signal y(n)1801, and providing a sensing result 1838. An algorithm is based on theuse of the PN sequences PN63 and PN511 in the ATSC Data Field Syncsegment. The algorithm can be described as comprising essentially twodistinct parts, an upper part 1851 and a lower part 1852. The upper part1851 can compute a signal auto-correlation using the 3 repeated PN63sequences (refer to Data Field Sync segment format in FIG. 5). Theauto-correlation can generate a frequency offset estimation that can beused to compensate the input signal so that signal cross-correlationwith known PN63 and/or PN511 sequences can be performed as shown in thelower part 1852.

An auto-correlation operation can be expressed in the following formula:

$\begin{matrix}{{J(n)} = {\sum\limits_{l = {n - 62}}^{n}{y*\left( {l - 63} \right){y(l)}}}} & (10)\end{matrix}$

Here y(l) is a received signal and y(l−63) is an input signal delayed by63 sampling cycles; it is the output of a 63-element delay line Delay 631802. Conjugation, multiplication, and summing operations of Equation(10) can be performed by the respectively corresponding modulesConjugate 1804, Multiply 1806, and Running Sum 63 1808. J is the outputof Running Sum 63 1808.

An auto-correlation magnitude |J(n)| curve can be expected to have aplateau when the input y(n) enters the PN63 region within the Data FieldSync segment, as illustrated in FIG. 19 where |J(n)| is shown as thethicker curve. The auto-correlation magnitude |J(n)| normalized by theaverage signal power can be compared against a predefined threshold inorder to determine whether such a situation has occurred.

An average signal power can be computed by elements of diagram 1800,including a Magnitude Square 1810 module and an Averaging 1812 module.An average signal power can be expressed as:

$\begin{matrix}{{P(n)} = {\frac{1}{N}{\sum\limits_{l = {n - {({N - 1})}}}^{n}{{y(l)}}^{2}}}} & (11)\end{matrix}$

A direct implementation of averaging in Equation (11) can be achievedemploying numerous techniques well known in the art. In a reducedhardware implementation embodiment, an IIR with a forgetting factor 1/Ncan be employed. The IIR filter output can serve as a closeapproximation to an actual running sum output. Operation of an averagingIIR filter embodiment can be expressed as:

$\begin{matrix}{{P(n)} = {{\frac{1}{N}{{y(n)}}^{2}} + {\left( {1 - \frac{1}{N}} \right){P\left( {n - 1} \right)}}}} & (12)\end{matrix}$

where P(n−1) is the IIR filter output for the sampling cycle immediatelyprevious. It can be appreciated that averaging depth N is a parametercommon to a plurality of implementations. In some embodiments, arelatively large N can be employed in order to achieve specifiedperformance criteria for computation of average power. For example, Ncan be chosen to be 500.

An Autocorr. Threshold 1814 module can compare normalizedauto-correlation magnitude, i.e.:

$\begin{matrix}\frac{{J(n)}}{P(n)} & (13)\end{matrix}$

against a specified pre-defined threshold, e.g. 0.45. A threshold can bespecified such that values exceeding the threshold indicate a specifiedreasonable confidence of entering a PN63 region of a Data Field Syncsegment, whereupon a peak search operation can follow.

A Peak Search 1822 operation can be controlled by two time stamps: peaksearch start p0 1816 and peak search end p1 1818. These time stamps canbe generated by a Control Timer 1820 module. In the event that anautocorrelation threshold is exceeded at sampling index n, a requirementcan follow that:

p0≧n   (14)

A p0 1816 value can typically be chosen to be equal to n. The durationbetween time stamps:

T _(PS) =p1−p0   (15)

can be specified to be long enough so that it can cover a plateau regionof normalized auto-correlation magnitude as shown in diagram 1900. Forexample, T_(PS) can be chosen to be twice the PN63 length, i.e. 126.

Between the two time stamps p0 and p1, a Peak Search 1822 module can beactivated in order to search for an auto-correlation J(n) thatcorresponds to a maximum normalized auto-correlation magnitude, asexpressed in Equation (13). This maximum auto-correlation can be used toestimate a frequency offset and/or to generate a phase increment inorder to derotate an input signal in order to compensate for a frequencyoffset. Taking into account frequency offset, the auto-correlation canbe expressed as:

$\begin{matrix}{{{y(n)}\mspace{14mu} {J(n)}} = {^{j\; 2\pi \; {\rho \cdot 63}}{\sum\limits_{l = {n - 63}}^{n}{y*\left( {l - 63} \right){y(l)}}}}} & (16)\end{matrix}$

with per sample frequency offset ρ=Δƒ_(c)T_(s) where Δƒ_(c) is thefrequency offset and T_(s) is the sampling period. Referring to diagram1900, for a maximum J(n) (within the plateau region) it can be expectedthat

y(l−63)=y(l)   (17)

if noise is neglected. Thus Equation (16) becomes:

$\begin{matrix}{^{{j2\pi\rho} \cdot 63} = \frac{J(n)}{\sum\limits_{l = {n - 63}}^{n}{{y(l)}}^{2}}} & (18)\end{matrix}$

and a frequency offset per sample can be estimated:

$\begin{matrix}{{2{\pi\rho}} = \frac{\arg \left\lbrack {J(n)} \right\rbrack}{63}} & (19)\end{matrix}$

The above computation can be performed by the Phase Incre. Comp. 1824module. The module can generate an incrementing phase factor:

e^(−j2πρn)   (20)

that can be used to compensate input signal samples y(n) 1801 forfrequency offset when (complex) multiplied by this factor. The complexmultiplier 1826 can perform this multiplication on a delayed version ofthe input signal.

ATSC signal sensing can be performed by correlating compensated inputsignal samples with known PN sequence patterns. PN511 and/or PN63 can beused for such correlation. In order for correlation to be performedafter frequency offset is computed, a start time of such correlation c0can be required to satisfy:

c0≧p1   (21)

In some embodiments a delay element Delay X 1828 can be provided betweeninput signal samples and complex multiplier 1826 employed for frequencyoffset compensation. This delay element can ensure that frequency offsetcompensation will not be missed on the target PN sequence pattern.

In one example, the desired PN sequence pattern can be PN511, referringto diagram 1900. Assuming that the start of PN511 is n0, a length X ofthe delay element Delay X 1828 can be required to satisfy:

X≧c0−n0≧p1−n0=T _(PS)+(p0−n0)   (22)

Assuming that p0 occurs before the end of a third PN63, then a maximumdistance between p0 and n0 can be 511+63×3=700. The length of the delayelement can be required to be at least:

X=T _(PS)+700   (23)

If the desired PN sequence is the third PN63, a maximum distance betweenp0 and n0 is 63, and thus a required length of delay element can be:

X=T _(PS)+63   (24)

Note that as discussed previously, PN511 can give higher coding gainthan PN63 (27 dB versus 18 dB). However, if a desired coding gain isless than 18 dB, the third PN63 can be selected as the targetcorrelation pattern in order to minimize computational complexity.

Compensated signal samples can be piped through a shift register ShiftRegister Y 1830 whose size can be equal to the length of a target PNsequence, e.g. 511 for PN511 and 63 for PN63. A signal pattern in theshift register can then be correlated with a target PN sequence togenerate a sensing metric for thresholding, i.e.

$\begin{matrix}{{M(n)} = {\sum\limits_{l = 0}^{Y - 1}{{s\left( {n - l} \right)} \cdot {b\left( {Y - 1 - l} \right)}}}} & (25)\end{matrix}$

Here s(n−l) is the I-th element of the shift register and b(l) is theI-th element of the PN sequence. Y can be take on the value of 511 or63, depending on the PN sequence used. Equation (25) can be implementedby modules Shift Register Y 1830, vector multiplier 1832, PN_Y 1834, andSum Y 1836. PN_Y 1834 can provide a PN sequence to the vector multiplier1832. Sum Y 1836 can perform a summing operation on input provided bythe vector multiplier 1832 and provide a result to a Sensing Threshold1840 module.

Power of an averaged sensing metric normalized by an average signalpower, i.e.:

$\begin{matrix}\frac{{{\frac{1}{Y}{M(n)}}}^{2}}{P(n)} & (26)\end{matrix}$

can then be compared with a threshold to determined ATSC signalpresence. For example, such a threshold could have a value of 0.25.Threshold comparisons can be performed by the Sensing Threshold 1840module.

Sensing Threshold 1840 module can be activated between times representedby time stamps s0 1842 and s1 1844; these time stamps can be generatedby a Control Timer 1820 module. For example, s0 can be chosen to be p1,and, s1 can be chosen to ensure that a desired PN sequence completelypasses through the shift register.

NTSC Signal Sensing:

A NTSC (analog TV) signal can contain narrowband video, chroma, andaudio carriers. Video, chroma, and audio carriers can be located at 1.25MHz, 4.83 MHz, 5.75 MHz from the left band edge respectively, asdepicted in the diagram 600 of FIG. 6.

Since a video carrier can have significantly higher power than othercarriers in the composite signal, some embodiments of NTSC signalsensing can be based on video carrier sensing; hence chroma and audiocarriers are neglected in the following analysis. A transmitted TVsignal can be approximated in the following form:

$\begin{matrix}{{s(n)} = {\underset{\underset{{Video}\mspace{14mu} {carrier}}{}}{^{{j2\pi}\; \frac{k_{v}}{N}n}} + {\mu \; \underset{\underset{Data}{}}{x(n)}}}} & (27)\end{matrix}$

where μ is the modulation index [A. B. Carlson, Communication Systems.

Electrical and Electronic Engineering Series, McGraw-Hill, third ed.,1986]. Data signal samples x can be assumed to be independent with unitpower, i.e.

E[x(n)x*(m)]=δ(n−m)   (28)

A sensing operator can be applied to a received signal in order togenerate a sensing metric:

$\begin{matrix}\begin{matrix}{{M(m)} = {\underset{\underset{{NTSC}\mspace{14mu} {sensing}\mspace{14mu} {operator}}{}}{\frac{1}{N}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}^{{- {j2\pi}}\; \frac{k_{v}}{N}{({n - m})}}}}{y(n)}}} \\{= {^{{j2\pi}\; \frac{k_{v}}{N}m}\frac{1}{N}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}{^{{- {j2\pi}}\; \frac{k_{v}}{N}n}\left\lbrack {{\sum\limits_{p = {- \infty}}^{\infty}{{c(p)}{s\left( {n - p} \right)}}} + {z(n)}} \right\rbrack}}}}\end{matrix} & (29)\end{matrix}$

It can be appreciated that such a sensing operator can be essentiallyequivalent to an FFT-based narrowband filter. A multipath delay spreadcan be assumed to be time-limited; it follows that c(p) can be nonzeroonly when

|p|<L   (30)

Terms can be considered

$\begin{matrix}{{\frac{1}{N}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}{^{{- {j2\pi}}\; \frac{k_{v}}{N}n}{\sum\limits_{p = {- \infty}}^{\infty}{{c(p)}{s\left( {n - p} \right)}}}}}} = {{\sum\limits_{{p} < L}{{c(p)}^{{- {j2\pi}}\; \frac{k_{v}}{N}p}\frac{1}{N}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}{^{{- {j2\pi}}\; \frac{k_{v}}{N}{({n - p})}}{s\left( {n - p} \right)}}}}} = {{\sum\limits_{{p} < L}{{c(p)}^{{- {j2\pi}}\; \frac{k_{v}}{N}p}\frac{1}{N}{\sum\limits_{q = {{{- N}/2} - p}}^{{N/2} - 1 - p}{^{{- {j2\pi}}\; \frac{k_{v}}{N}q}{s(q)}}}}} = {{{\sum\limits_{{p} < L}{{c(p)}^{{- {j2\pi}}\; \frac{k_{v}}{N}p} \begin{Bmatrix}{\frac{1}{N} {\sum\limits_{q = {{{- N}/2} - p}}^{{{- N}/2} - 1} +}} \\{\frac{1}{N}{\sum\limits_{q = {{- N}/2}}^{{N/2} - 1}{{- \frac{1}{N}}\sum\limits_{q = {{{- N}/2} - p}}^{{N/2} - 1}}}}\end{Bmatrix} ^{{- {j2\pi}}\; \frac{k_{v}}{N}q} {s( q)}}} \approx {\sum\limits_{{p} < L}{{c(p)}^{{- {j2\pi}}\; \frac{k_{v}}{N}p}\frac{1}{N}{\sum\limits_{q = {{- N}/2}}^{{N/2} - 1}{^{{- {j2\pi}}\; \frac{k_{v}}{N}q}{s(q)}}}}}} = {{{NC}\left( k_{v} \right)}{S\left( k_{v} \right)}}}}}} & (31)\end{matrix}$

where an approximation can be taken assuming

N>>L>|p|  (32)

and for a channel frequency response C(k_(ν)) and a spectral signalvalue S(k_(ν)) at a video carrier frequency

$f = \frac{k_{v}}{{NT}_{S}}$

where:

$\begin{matrix}{{C\left( k_{v} \right)} = {\frac{1}{N}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}{{c(n)}^{{- {j2\pi}}\; \frac{k_{v}}{N}n}}}}} & (33) \\{{S\left( k_{v} \right)} = {\frac{1}{N}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}{{s(n)}^{{- {j2\pi}}\; \frac{k_{v}}{N}n}}}}} & (34)\end{matrix}$

Similarly, spectral noise Z(k_(ν)) at a frequency k_(ν) can be expressedas

$\begin{matrix}{{Z\left( k_{v} \right)} = {\frac{1}{N}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}{{z(n)}^{{- j}\; 2\pi \; \frac{k_{v}}{N}n}}}}} & (35)\end{matrix}$

Given the definitions above, a sensing metric M(m) can be expressed as

$\begin{matrix}{{M(m)} = {^{{j2\pi}\; \frac{k_{v}}{N}m}\left\lbrack {{{{NC}\left( k_{v} \right)}{S\left( k_{v} \right)}} + {Z\left( k_{v} \right)}} \right\rbrack}} & (36)\end{matrix}$

S(k_(ν)) can be further expanded using equation (27):

$\begin{matrix}{\begin{matrix}{{S\left( k_{v} \right)} = {\frac{1}{N}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}{\left\lbrack {^{{j2\pi}\; \frac{k_{v}}{N}n} + {\mu \; {x(n)}}} \right\rbrack ^{{- {j2\pi}}\; \frac{k_{v}}{N}n}}}}} \\{= {1 + {\mu \; {X\left( k_{v} \right)}}}}\end{matrix}{where}} & (37) \\{{X\left( k_{v} \right)} = {\frac{1}{N}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}{{x(n)}^{{- {j2\pi}}\; \frac{k_{v}}{N}n}}}}} & (38)\end{matrix}$

A sensing metric M(m) can then be expressed as

$\begin{matrix}{{M(m)} = {^{{j2\pi}\; \frac{k_{v}}{N}m}\left\lbrack {\underset{\underset{Signal}{}}{{NC}\left( k_{v} \right)} + \underset{\underset{Noise}{}}{{\mu \; {NC}\left( k_{v} \right){X\left( k_{v} \right)}} + {Z\left( k_{v} \right)}}} \right\rbrack}} & (39)\end{matrix}$

Note that in some embodiments a NTSC data signal can be considered asnoise when sensing is performed, since it is not being decoded. Since adata signal and noise are independent, a total sensing metric noisepower can be expressed as:

$\begin{matrix}{{{\mu^{2}N^{2}{{C\left( k_{v} \right)}}^{2}{E\left\lbrack {{X\left( k_{v} \right)}}^{2} \right\rbrack}} + {E\left\lbrack {{Z\left( k_{v} \right)}}^{2} \right\rbrack}}{where}} & (40) \\\begin{matrix}{{E\left\lbrack {{X\left( k_{v} \right)}}^{2} \right\rbrack} = {E\left\lbrack {{\frac{1}{N}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}{{x(n)}^{{- {j2\pi}}\; \frac{k_{v}}{N}n}}}}}^{2} \right\rbrack}} \\{= {\frac{1}{N^{2}}{\sum\limits_{n,{m = {{- N}/2}}}^{{N/2} - 1}{^{{- {j2\pi}}\; \frac{k_{v}}{N}{({n - m})}}{E\left\lbrack {{x(n)}{x^{*}(m)}} \right\rbrack}}}}} \\{= \frac{1}{N}}\end{matrix} & (41)\end{matrix}$

and similarly

$\begin{matrix}{{E\left\lbrack {{Z\left( k_{v} \right)}}^{2} \right\rbrack} = \frac{\sigma^{2}}{N}} & (42)\end{matrix}$

An NTSC data signal power can be expressed

$\begin{matrix}{{E\left\lbrack {{Z\left( k_{v} \right)}}^{2} \right\rbrack} = \frac{\sigma^{2}}{N}} & (42)\end{matrix}$

Using an IFFT, the NTSC data signal power can further be expressed as

$\begin{matrix}\begin{matrix}{{\mu^{2}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}{{c(n)}}^{2}}} = {\mu^{2}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}\left\lbrack {\sum\limits_{k = {{- N}/2}}^{{N/2} - 1}{{C(k)}^{{j2\pi}\frac{k}{n}}}} \right\rbrack}}} \\{\left\lbrack {\sum\limits_{l = {{- N}/2}}^{{N/2} - 1}{{C^{*}(l)}^{{- {j2\pi}}\frac{1}{n}n}}} \right\rbrack} \\{= {\mu^{2}{\sum\limits_{k = {{- N}/2}}^{{N/2} - 1}{\sum\limits_{l = {{- N}/2}}^{{N/2} - 1}{{C(k)}{C^{*}(l)}{\sum\limits_{n = {{- N}/2}}^{{N/2} - 1}^{{j2\pi}\frac{k - l}{N}n}}}}}}} \\{= {\mu^{2}{\sum\limits_{k = {{- N}/2}}^{{N/2} - 1}{\sum\limits_{l = {{- N}/2}}^{{N/2} - 1}{{C(k)}{C^{*}(l)}N\; {\delta \left( {k - l} \right)}}}}}} \\{= {\mu^{2}N{\sum\limits_{k = {{- N}/2}}^{N/2}{{C(k)}}^{2}}}}\end{matrix} & (44)\end{matrix}$

A data signal SNR can then be expressed as

$\begin{matrix}{{SNR} = {\frac{E\left\lbrack {{{{c(n)} \otimes \mu}\; {x(n)}}}^{2} \right\rbrack}{E\left\lbrack {{z(n)}}^{2} \right\rbrack} = \frac{\mu^{2}N{\sum\limits_{k = {{- N}/2}}^{N/2}{{C(k)}}^{2}}}{\sigma^{2}}}} & (45)\end{matrix}$

A SNR of the a metric expressed in Equation (39) can then be expressed

$\begin{matrix}{\begin{matrix}{{SNR}_{M} = \frac{N^{2}{{C\left( k_{v} \right)}}^{2}}{{\mu^{2}N^{2}{{C\left( k_{v} \right)}}^{2}{E\left\lbrack {{X\left( k_{v} \right)}}^{2} \right\rbrack}} + {E\left\lbrack {{Z\left( k_{v} \right)}}^{2} \right\rbrack}}} \\{= \frac{N^{2}{{C\left( k_{v} \right)}}^{2}}{{\mu^{2}N{{C\left( k_{v} \right)}}^{2}} + \frac{\sigma^{2}}{N}}} \\{= \frac{N^{2}{{C\left( k_{v} \right)}}^{2}}{{\mu^{2}N{{C\left( k_{v} \right)}}^{2}} + \frac{\mu^{2}{\sum\limits_{k = {{- N}/2}}^{N/2}{{C\left( k_{v} \right)}}^{2}}}{SNR}}} \\{= {\frac{1}{\mu^{2}}\frac{N \cdot {SNR}}{{SNR} + \eta}}}\end{matrix}{where}} & (46) \\{\eta = \frac{\sum\limits_{k = {{- N}/2}}^{N/2}{{C(k)}}^{2}}{N{{C\left( k_{v} \right)}}^{2}}} & (47)\end{matrix}$

It follows that a sensing gain can be expressed

$\begin{matrix}{{10\log_{10}\frac{{SNR}_{M}}{SNR}} = {10{\log_{10}\left( {\frac{1}{\mu^{2}}\frac{N}{{SNR} + \eta}} \right)}}} & (48)\end{matrix}$

In some embodiments, a sensing gain can decrease with an increasing SNR.In some embodiments this property can be neglected since at high SNR,sensing can be relatively successful essentially regardless of a sensinggain value. A factor η can be a measure of multipath fading on a videocarrier. When the fading is significant, the sensing gain can becomesmaller. The graph 700 of FIG. 7 shows sensing gain contours atdifferent SNRs and fading levels η where N=256 and μ=0.875 (TV videosignal modulation index according to [A. B. Carlson, op. cit.]). It canbe appreciated that sensing gain can increase with increasing N. In somefading environments, a narrowband fade could be larger than 30 dB [T. S.Rappaport, Wireless Communications Principles and Practice.Prentice-Hall, 1996]. Rather than simply increasing the correlationdepth N in order to ameliorate this effect, it can be advantageous insome embodiments to share sensing information with other users. In someembodiments, multipath fading can be a local phenomenon whose effectscan be statistically reduced by combining sensing results from differentlocations.

Energy Sensing:

Energy sensing can be employed as a fundamental method of signaldetection in some embodiments; any information-conveying signal hasfinite energy. In some embodiments, energy-based sensing can suffer froma drawback of a longer convergence time as compared to phase-basedsensing, as discussed herein regards DTV and NTSC signal sensing.Energy-based sensing can achieve salutary specified performance levelsunder the conditions of a high SNR, in some embodiments. Furthermore, insome embodiments energy-based sensing can be used to estimate a SNR. Areceived signal y(n) can be expressed as

y(n)=x(n)+z(n)   (49)

A sequence of signal samples x(n) can be assumed to be independent, inorder to simplify derivation. Correlation between signal samples x(n) s,e.g. due to multipath channel memory effect, can (only) improve sensingperformance. Since the noise sample z(n) s are independent, the receivedsignal sample y(n) s are independent.

Consider a signal energy S obtained by averaging:

$\begin{matrix}{S = {\frac{1}{N_{B}}{\sum\limits_{n = 1}^{N_{B}}{{y(n)}}^{2}}}} & (50)\end{matrix}$

where N_(B) can be an averaging buffer size. Since |y(n)|² can be asequence of independent and identically distributed (IID) randomvariables with mean and variance, it follows that:

E[|y(n)|²]=μ  (51)

E[|y(n)|⁴]=σ²   (52)

Assuming N_(B)>>1, according to the central limit theorem, S can beapproximated as a Gaussian random variable with mean μ_(S) and varianceσ_(S) ²:

$\begin{matrix}{\mu_{S} = \mu} & (53) \\{\sigma_{S}^{2} = \frac{\sigma^{2}}{N_{B}}} & (54)\end{matrix}$

PFD vs. PLD:When there is no signal present, i.e. x(n)=0, the result of averagingcan be expressed:

$\begin{matrix}{S_{0} = {\frac{1}{N_{B}}{\sum\limits_{n = 1}^{N_{B}}{{z(n)}}^{2}}}} & (55)\end{matrix}$

When there is a signal present, the result of the averaging can beexpressed

$\begin{matrix}{S_{1} = {\frac{1}{N_{B}}{\sum\limits_{n = 1}^{N_{B}}{{y(n)}}^{2}}}} & (56)\end{matrix}$

where y(n) is given by Equation (49). A threshold S_(T) can be used todetermine whether a signal is present. A value of (averaged) signalenergy exceeding the threshold can correspond to the presence of asignal. A value of (averaged) signal energy not exceeding the thresholdcan correspond to the absence of a signal.

As illustrated in the graph 800 of FIG. 8, a probability of falsedetection (PFD) is the probability that the sensing indicates signalpresence (i.e. energy average of Equation (50) above threshold) whenthere is noise but essentially no signal present. A probability of lossdetection (PLD) is the probability that sensing indicates absence of asignal (i.e. energy average Equation (50) below threshold) when there isa signal present. A threshold can be set relatively high, thus reducingPFD at the expense of increased PLD. The threshold can be set relativelylow, thus reducing PLD at the expense of increased PFD. Thus an optimalthreshold S_(T) can be selected at an operating point where PFD isessentially equal to PLD.

Sensing Error Floor Calculation:

A sensing error floor (SEF) can be defined as the PFD, or equivalentlyPLD, at an optimal threshold. A general expression for a sensing errorfloor can be derived. Referring to FIG. 8, a PFD can be expressed usinga Q function:

$\begin{matrix}{{PFD} = {Q\left( \frac{S_{T} - \mu_{S_{0}}}{\sigma_{S_{0}}} \right)}} & (57)\end{matrix}$

and a PLD can be expressed using a Q function:

$\begin{matrix}{{PLD} = {{1 - {Q\left( \frac{S_{T} - \mu_{S_{1}}}{\sigma_{S_{1}}} \right)}} = {Q\left( {- \frac{S_{T} - \mu_{S_{1}}}{\sigma_{S_{1}}}} \right)}}} & (58)\end{matrix}$

using a symmetry property of the Q function. (A Q-function can bedefined as the complement of a standard normal cumulative distributionfunction.) At the sensing error floor, the PFD is equal to the PLD, i.e.

$\begin{matrix}{{Q\left( \frac{S_{T} - \mu_{S_{0}}}{\sigma_{S_{0}}} \right)} = {\left. {Q\left( {- \frac{S_{T} - \mu_{S_{1}}}{\sigma_{S_{1}}}} \right)}\Rightarrow\frac{S_{T} - \mu_{S_{0}}}{\sigma_{S_{0}}} \right. = {- \frac{S_{T} - \mu_{S_{1}}}{\sigma_{S_{1}}}}}} & (59)\end{matrix}$

which leads to a solution of the threshold

$\begin{matrix}{S_{T} = \frac{{\mu_{S_{0}}\sigma_{S_{1}}} + {\mu_{S_{1}}\sigma_{S_{0}}}}{\sigma_{S_{0}} + \sigma_{S_{1}}}} & (60)\end{matrix}$

Substituting from Equation (60) for S_(T) in Equation (57), the sensingerror floor can be expressed

$\begin{matrix}{{SEF} = {Q\left( \frac{\mu_{S_{1}} - \mu_{S_{0}}}{\sigma_{S_{0}} + \sigma_{S_{1}}} \right)}} & (61)\end{matrix}$

Using Equations (53) and (54), the sensing error floor can be furtherexpressed as

$\begin{matrix}{{{SEF} = {{Q\left( \frac{\mu_{1} - \mu_{0}}{\frac{\sigma_{0}}{\sqrt{N_{B}}} + \frac{\sigma_{1}}{\sqrt{N_{B}}}} \right)} = {Q\left( {\sqrt{N_{B}}\frac{\mu_{1} - \mu_{0}}{\sigma_{0} + \sigma_{1}}} \right)}}}{where}} & (62) \\{\mu_{0} = {E\left\lbrack {{z(n)}}^{2} \right\rbrack}} & (63) \\{\sigma_{0} = \sqrt{\left. {E\left\lbrack {{{z(n)}}^{2} - \mu_{0}} \right)}^{2} \right\rbrack}} & (64) \\{\mu_{1} = {E\left\lbrack {{y(n)}}^{2} \right\rbrack}} & (65) \\{\sigma_{1} = \sqrt{E\left\lbrack \left( {{{y(n)}}^{2} - \mu_{1}} \right)^{2} \right\rbrack}} & (66)\end{matrix}$

(For simplicity, in the following derivations, the sample index n isomitted.) The noise power can be assumed to be

μ₀ =E[|z| ² ]=P _(N)   (67)

The noise covariance can be expressed

$\begin{matrix}{\begin{matrix}{\sigma_{0}^{2} = {E\left\lbrack \left( {{z}^{2} - \mu_{0}} \right)^{2} \right\rbrack}} \\{= {E\left\lbrack {{z}^{4} - {2\mu_{0}{z}^{2}} + \mu_{0}^{2}} \right\rbrack}} \\{= {{E\left\lbrack {z}^{4} \right\rbrack} - \mu_{0}^{2}}} \\{= {\left( {\alpha_{{complex}\text{-}{gaussian}} - 1} \right)P_{N}^{2}}} \\{= {\left( {2 - 1} \right)P_{N}^{2}}} \\{= P_{N}^{2}}\end{matrix}{where}} & (68) \\{\alpha_{{complex} - {gaussin}} = \frac{E\left\lbrack {z}^{4} \right\rbrack}{\left\{ {E\left\lbrack {z}^{2} \right\rbrack} \right\}^{2}}} & (69)\end{matrix}$

The signal power can be assumed to be

P _(S) =E[|x| ²]  (70)

so that

μ₁ =E[|y| ² ]=E[|x| ² ]+E[|z| ² ]=P _(S) +P _(N)   (71)

Finally, the covariance

$\begin{matrix}\begin{matrix}{\sigma_{1}^{2} = {E\left\lbrack \left( {{y}^{2} - \mu_{1}} \right)^{2} \right\rbrack}} \\{= {{E\left\lbrack {y}^{4} \right\rbrack} - \mu_{1}^{2}}}\end{matrix} & (72)\end{matrix}$

E[|y|⁴] can be expanded as

$\begin{matrix}\begin{matrix}{{E\left\lbrack {y}^{4} \right\rbrack} = {E\left\lbrack \left\{ {\left( {x + z} \right)\left( {x^{*} + z^{*}} \right)} \right\}^{2} \right\rbrack}} \\{= {E\left\lbrack \left( {{x}^{2} + {z}^{2} + {xz}^{*} + {zx}^{*}} \right)^{2} \right\rbrack}} \\{= {E\left\lbrack {{x}^{4} + {z}^{4} + {x^{2}\left( z^{*} \right)}^{2} + {\left( x^{*} \right)^{2}z^{2}} + {2{x}^{2}{z}^{2}} +} \right.}} \\\left. {{2{x}^{2}{xz}^{*}} + {2{x}^{2}x^{*}z} + {2x{z}^{2}z^{*}} + {2x^{*}{z}^{2}z} + {2{x}^{2}{z}^{2}}} \right\rbrack\end{matrix} & (73)\end{matrix}$

Assuming E[x]=E[z]=0 all terms involving the first-order of x and z inequation (73) can vanish after an expectation operation. Since z is acomplex Gaussian random variable, i.e. z=u+jv, it follows that

E[z ² ]=E[u ² −v ²]+2jE[u]E[v]=0=E[(z*)²]  (74)

and thus all terms involving the second-order of z in equation (73) canvanish after expectation. Equation (73) can then be simplified to:

E[|y| ⁴ ]=E[51 x| ⁴ ]+E[|z| ⁴]+4E[|x| ²]4E[|z| ²]  (75)

For a complex Gaussian random variable z,

E[|z| ⁴]2{E[|z| ²]}²=2P _(N) ²   (76)

For

E[|x| ⁴ ]=α{E[|x| ²]}² =αP _(S) ²   (77)

Equation (75) can be expressed as

E[|y| ⁴ ]=αP _(S) ²+2P _(N) ²+4P _(N) P _(S)   (78)

Substituting the above in equation (72) and using equation (71), itfollows that

$\begin{matrix}\begin{matrix}{\sigma_{1}^{2} = {{E\left\lbrack {y}^{4} \right\rbrack} - \mu_{1}^{2}}} \\{= {{\alpha \; P_{S}^{2}} + {2P_{N}^{2}} + {4P_{N}P_{S}} - \left( {P_{N} + P_{S}} \right)^{2}}} \\{= {{\left( {\alpha - 1} \right)P_{S}^{2}} + {2P_{N}P_{S}} + P_{N}^{2}}}\end{matrix} & (79)\end{matrix}$

Using equations (67) (68) (71), and (79), the sensing error floor SEF ofequation (62) can be alternatively expressed as

$\begin{matrix}\begin{matrix}{{SEF} = {Q\left\lbrack {\sqrt{N_{B}}\frac{P_{S}}{P_{N} + \sqrt{{\left( {\alpha - 1} \right)P_{S}^{2}} + {2P_{N}P_{S}} + P_{N}^{2}}}} \right\rbrack}} \\{= {Q\left\lbrack {\sqrt{N_{B}}\frac{SNR}{1 + \sqrt{{\left( {\alpha - 1} \right){SNR}^{2}} + {2 \cdot {SNR}} + 1}}} \right\rbrack}}\end{matrix} & (80)\end{matrix}$

α s for some typical types of signals are shown in the following table:

Type Example α Gaussian White noise, OFDM 2 signal Uniform Quantizationnoise 1.4 Amplitude- 64 QAM 1.38 modulated Amplitude- 16 QAM 1.32modulated Phase- BPSK, QPSK, 8-PSK 1 modulatedAssuming α=2, a SEF can be simplified to

$\begin{matrix}{{SEF} = {Q\left( {\sqrt{N_{B}}\frac{SNR}{{SNR} + 2}} \right)}} & (81)\end{matrix}$

The graph 900 of FIG. 9 shows the sensing error floor contours atvarious SNRs and averaging buffer sizes. Note that the error floor isexpressed in dB units, e.g. −20 dB corresponding to an SEF of 0.01. WhenSNR<<1, the argument of the Q function in equation (81) can beapproximated as

$\begin{matrix}{\sqrt{N_{B}}\frac{SNR}{2}} & (82)\end{matrix}$

Thus, in some embodiments a linear decrease in SNR can be met by aquadratic increase in buffer size N_(B) in order to maintain a specifiedSEF value. When SNR>>1, the argument of the Q function in equation (81)can be approximated as √{square root over (N_(B))} independent of SNR.

Energy-Based Sensing:

Energy-based sensing is a generic sensing method that can apply to manyclasses of signals, since any information-bearing signals haveessentially nonzero energy. Energy-based sensing can work well when anoise signal is far smaller in energy than a target signal. However, insome embodiments, the reliability of energy-based sensing can besignificantly impaired when the target signal power approaches the noisefloor. In addition, since energy sensing does not differentiate signaltypes, energy-based sensing can be susceptible to false detection, e.g.due to impulse noise and/or other types of radio activity.

In an embodiment of energy-based sensing, average signal power within aspecified target channel can be computed. Signal power can then becompared to a noise floor and can be thresholded to determine signalpresence. The computation can also lead to a channel SNR estimation thatcan be useful elsewhere.

A block diagram 2100 for some embodiments of energy sensing is depictedin FIG. 21. The upper branch of the graph illustrates computation ofsignal power in a target channel. A filter block 2102 can essentiallyprovide a significant information-bearing portion of signal spectrumwhile removing an unreliable portion of the signal spectrum (e.g.channel edges which can be contaminated by signal leakages from adjacentchannels). For a signal after the filter y(n), a target channel signalpower can be computed as:

$\begin{matrix}{{P(n)} = {\frac{1}{N}{\sum\limits_{l = {n - {({N - 1})}}}^{n}{{y(l)}}^{2}}}} & (83)\end{matrix}$

where N corresponds (again) to averaging depth. Equation (83) can beimplemented by Magnitude Square 2004 and Averaging 2006 modules.Magnitude Square 2004 can perform a power computation function |y(l)|²as shown in Equation (83) and whose implementations are discussedherein. Averaging 2006 can perform an averaging function whoseimplementations are discussed herein and particularly regarding diagrams1800.

A key part of an energy sensing algorithm can be computation of noisepower. A target signal power can be compared against a computed noisepower. For a TV signal, noise power can be computed using a referencechannel that contains essentially no signal transmission. In the TVband, Channel 37 is typically reserved for astronomy observations;typically essentially no signal transmission occurs. This channel can beused as a reference channel. Signal power computation on the referencechannel can be essentially the same as that for the target channel, asshown in the lower branch of the graph. The reference channel power canbe computed as:

$\begin{matrix}{{P_{0}(n)} = {\frac{1}{N}{\sum\limits_{l = {n - {({N - 1})}}}^{n}{{y_{0}(l)}}^{2}}}} & (84)\end{matrix}$

where y₀(n) is an input signal on the reference channel after filtering.A sensing decision can be made by comparing target channel signal powerP(n) against the reference channel signal power P₀(n) using a specifiedthreshold. A signal-to-noise ratio can be computed as:

$\begin{matrix}{{{SNR}(n)} = \frac{{P(n)} - {P_{0}(n)}}{P_{0}(n)}} & (85)\end{matrix}$

Threshold or Div. 2108 can provide a sensing result by comparing and/orthresholding the averaged power of a target channel signal (provided byAveraging 2106) with an averaged power of a reference channel. A signalprocessing chain comprising Filter 2112, Magnitude Square 2114, andAveraging 2116 can provide a static and/or a dynamic averaged power fora reference channel. A dynamic averaged power can also be otherwisespecified and/or computed as discussed herein. Filter 2112 can providefiltering for a reference channel as Filter 2102 does for a targetchannel. Magnitude Square 2114 and Averaging 2116 modules can providepower computation and averaging to the reference channel signal path asare provided to the target channel signal path by the correspondingmodules 2104 2106. In some embodiments Threshold or Div. 2108 module canprovide a SNR result as specified in Equation (85).

System and Network Layer Analysis:

Some important system and network layer techniques are herein describedthat can be used in order to facilitate TV-band cognitive radiooperation. For example, since TV receivers are typically passive,reactive interference control by detecting their presence is essentiallynot possible in some embodiments. Instead, a proactive approach can betaken through collective sensing by networked cognitive users in orderto reduce interference in a statistical sense. To maximize the potentialof cognitive user transmission while limiting adjacent channelinterference to TV users, a cognitive user transmission power can becontrolled adaptively based on signal power measurements of adjacent TVchannels. A hidden terminal probability analysis can quantify a TV-bandhidden terminal probability, and is described herein. Collective sensingcan help reduce hidden terminal probability, and is described herein.Rate and range adaptation to optimize system throughput under variousfield conditions is described herein. Also herein described areembodiments of collective sensing and adaptive transmission powercontrol in a TV-band cognitive radio system.

Hidden Terminal Probability Analysis

Instances of a hidden terminal can pose a significant challenge to someembodiments of a TV-band cognitive system. In a TV-band cognitive radiosystem, TV stations and TV receivers can be primary users. Since TVreceivers typically do not transmit, detecting the presence of a TVreceiver is not straightforward. The hidden terminal problem in aTV-band cognitive system can alternatively be addressed in a statisticalfashion. First, the hidden terminal problem can be quantified in termsof a hidden terminal probability (HTP). A mechanism is herein describedto then reduce hidden terminal probability through sensing informationsharing amongst cognitive users; this approach is called collectivesensing.

A uniform propagation loss model is described herein, wherein signalpropagation loss can increase monotonically with distance. A case ofshadowing is herein described, where propagation loss can depend onpropagation distance and/or environmental attenuation.

HTP With No Shadowing:

A signal transmission from a primary user U₁ 1002 to another primaryuser U₂ 1004 in a propagation environment with uniform propagation lossis depicted in the diagram 1000 of FIG. 10. d₀ 1006 can represent amaximum distance within which a primary user can detect another primaryuser. d₁ 1008 can be a maximum distance within which a cognitive usercan detect a primary user. d₂ 1010 can be a maximum distance withinwhich a transmission from a cognitive user can be considered harmful toa primary user.

As depicted in the illustration, for a signal transmission from U₁ toU₂, a cognitive user who appears in the marked hidden terminal region A₁1012 cannot detect U₁ and can create harmful interference to U₂ uponchoosing to transmit. A worst case interference, i.e. the largest A₁,can result when U₂ is at a maximum distance d₀ from U₁.

Assuming a cognitive user area density of ρ, the hidden terminalprobability—the probability that at least one cognitive user appearingin the interference region A₁—can be expressed

P _(HT)=1−e ^(−ρA) ^(I)   (86)

as derived herein. Referring to FIG. 10, in order to completelyeliminate the interference can require that

d ₁ ≧d ₀ +d ₂   (87)

in which case A₁=0 and P_(HT)=0.

In a log-distance path loss model [T. S. Rappaport, op. cit.] , lossfrom a transmitter to a receiver at a distance r away can be expressedas

$\begin{matrix}{{\overset{\_}{L}(r)} = {{K\left( r_{0} \right)}\left( \frac{r}{r_{0}} \right)^{\alpha}}} & (88)\end{matrix}$

Here α is the path loss exponent characteristic of the propagationenvironment; r₀ is the distance from the transmitter to a close-inreference point; and K(r₀) is the loss from the transmitter to thereference point. For simplicity, the primary user and cognitive user canbe assumed to have essentially the same r₀ and K(r₀). It can beappreciated that such assumptions can effectively be equivalent toassuming that a TV station transmitter has the same elevation as acognitive user transmitter. Under these assumptions, a TV signal pathloss can be significantly overestimated.

As illustrated in the diagram 1100 of FIG. 11, primary user transmissionpower is P₀ 1102; cognitive user transmission power is P₁ 1104; aprimary user decoding threshold power is P_(D) 1106; a cognitive usersensing power can be P_(S) 1108; and a harmful interference thresholdpower level can be P_(H) 1110. Since losses for the distances d0 d1 d2(figure elements 1112 1114 1116, respectively) can be expressed:

L (d ₀)=P ₀ /P _(D)   (89)

L (d ₁)=P ₀ /P _(S)   (90)

L (d ₂)=P ₁ /P _(H)   (91)

An interference free condition of Equation (87) can be restated as:

$\begin{matrix}\left. {{r_{0}\left\lbrack {\frac{1}{K\left( r_{0} \right)}\frac{P_{0}}{P_{S}}} \right\rbrack}^{\frac{1}{\alpha}} \geq {{r_{0}\left\lbrack {\frac{1}{K\left( r_{0} \right)}\frac{P_{0}}{P_{D}}} \right\rbrack}^{\frac{1}{\alpha}} + {r_{0}\left\lbrack {\frac{1}{K\left( r_{0} \right)}\frac{P_{1}}{P_{H}}} \right\rbrack}^{\frac{1}{\alpha}}}}\mspace{20mu}\Rightarrow{\left( \frac{P_{D}}{P_{S}} \right)^{\frac{1}{\alpha}} \geq {1 + \frac{\left( \frac{P_{D}}{P_{H}} \right)^{\frac{1}{\alpha}}}{\left( \frac{P_{0}}{P_{1}} \right)^{\frac{1}{\alpha}}}}} \right. & (92)\end{matrix}$

Note that a sensing gain 1118 P_(D)/P_(S) can measure cognitive usersensing performance against primary user decoding performance. P₀/P₁ canbe a measure of primary user and cognitive user transmission powerdifference 1120. The graph 1200 of FIG. 12 plots sensing gainP_(D)/P_(S) 1118 values against transmission power difference P₀/P₁ 1120required in order to achieve an interference-free condition of Equation(92), assuming that P_(D)/P_(H)=30 dB. Curves 1220 1225 1230 1235 12401245 1250 are shown for path loss exponent α values of 2.0 2.5 3.0 3.54.0 4.5 5.0, respectively.

FIG. 12 illustrates an embodiment in which a typical TV stationtransmission power is approximately 100 kW and a cognitive user'smaximum transmission power is approximately 1 W (P₀/P₁=50 dB). In someembodiments an 8 dB P_(D)/P_(S) can ensure essentially interference-freeoperation under the conditions of typical path loss exponents.

HTP In Shadowing Environment:

A typical propagation environment can be non-uniform. Receivers disposedat essentially equivalent distances from a particular transmitter cansimultaneously experience different received signal strengths from thattransmitter. Variations can depend on signal paths between transmitterand receivers. This effect is called shadowing. The log-distance pathloss of Equation (88) can be expressed in dB form

L ^(dB)(r)=K ^(dB)(r ₀)−10 α log₁₀ r ₀30 10 α log₁₀ r   (93)

and an actual loss at a particular receiver can be modeled as

L ^(dB)(r)= L ^(dB)(r)+X _(σ) ^(dB)   (94)

where L ^(dB)(r) is considered as an average path loss while a zero-meanGaussian random variable X_(σ) ^(dB) with standard deviation σ canaccount for the effect of shadowing [T. S. Rappaport, op. cit.].

A signal transmission from primary user U₁ 1002 to primary user U₂ 1004is depicted in the diagram 1300 of FIG. 13. Given a distance between U₁and U₂ of r, a distance between a potentially interfering cognitive userand U₂ of Δ, and a distance between U₁ and the cognitive user of r′, itcan follow that due to the transmission power difference between U₁ andthe cognitive user, r>>Δ and

L (r)≈ L (r+Δ)≧ L (r′)≧ L (r)

L (r′)≈ L (r)   (95)

In some embodiments a potentially interfering cognitive user canexperience essentially the same path loss for a signal coming from U₁ asfor a signal coming from U₂. In another case, U₂ can be at a cell edge,i.e. r=d₀. Signal path loss from U₁ to a cognitive user can then beexpressed

L ^(dB)(d ₀)+X _(σ) ^(dB)   (96)

Successful sensing of the signal by the cognitive user can require

P ₀ ^(dB) −[ L ^(dB)(d ₀)+X _(σ) ^(dB) ]>P _(S) ^(dB)

X _(σ) ^(dB) <P _(D) ^(dB) −P _(S) ^(dB)

with a sensing success probability of

$\begin{matrix}{\Gamma = {\frac{1}{\sqrt{2\pi}\sigma}{\int_{- \infty}^{P_{D}^{dB} - P_{S}^{dB}}{^{- \frac{x^{2}}{2\sigma^{2}}}{x}}}}} & (97)\end{matrix}$

(P_(D) and P_(S) are in dB units and Γ is a probability value between 0and 1.) Because of shadowing, the interference region can extend beyondd₂ (from U₂), with interference probability decreasing with increasingdistance from U₂. An effective interference region A₁ 1302 around U₂ canbe assumed (for simplicity):

A₁=πd₁ ²   (98)

noting that d₁ 1304 can be on the same order as d₂ for this assumption.A cognitive user that is located in effective interference region A₁ andthat does not successfully sense a signal from U₁ can be identified as ahidden terminal.

Given cognitive user density ρ, sensing success probability Γ, andeffective interference region A₁, a hidden terminal probability in thecase of shadowing can be expressed

P _(HT)=1−e ^(−ρA) ¹ ^((1−Γ))   (99)

as derived herein. For a resulting constant A₁=πd₁ ²˜πd₂ ², anessentially zero HTP thus corresponds to Γ=1. Such a constraint couldrequire essentially infinite sensing gain P_(D)/P_(S) to meet aspecified performance criterion. For a finite P_(D)/P_(S), HTP inEquation (99) is monotonic with cognitive user density ρ. As ρincreases, HTP can exceed a tolerable specified performance level.HTP with Sensing Information Sharing:

In some embodiments the above dilemma can result from sensing beingperformed by a lone local cognitive user. If a user happens toexperience severe shadowing, the user may not be able to detect aprimary user signal even with relatively large sensing gain. Ascognitive user density increases, such a situation can become morelikely and can result in increased interference to a primary user.

This system behavior can be addressed in some embodiments by employingsensing information sharing between cognitive users. As an increasingnumber of cognitive users share their sensing results, the probabilityof shadowing can be reduced exponentially, and hidden terminalprobability can be similarly reduced. This approach can be calledcollective sensing by a cognitive user network.

Referring to FIG. 13, all cognitive users in an area A_(C) 1306 aroundU₂ and defined by radius d_(C) 1308,

A_(c)=πd_(C) ²   (100)

can share their sensing information so that a cognitive user located inA₁ 1302 transmits only under the condition that essentially none of thecognitive users in A_(C) 1306 sense a signal from U₁ 1002. AssumingA_(C)≧A₁, a hidden terminal probability can be expressed

P _(HT) =e ^(−ρA) ^(C) ^(Γ)[1−e ^(−ρA) ¹ ^((1−Γ))]  (101)

as derived herein.

Comparing Equation (101) with Equation (99), the factor e^(−ρA) ^(C)^(Γ)—a result of sensing information sharing—can help to drive down HTPas ρ increases. This effect is shown in the graph 1400 of FIG. 14 underthe assumption of A₁=A_(C). Γ can be calculated using Equation (97); ashadowing environment can be characterized by using a random Gaussianvariable X_(σ) ^(dB) with σ=11.8 dB based on an urban cellularmeasurement [T. S. Rappaport, op. cit.].

Graph 1400 depicts HTP value curves 1402, 1404, 1406, 1408, and 1410corresponding to specified sensing gains of 0 dB, 5 dB, 10 dB, 15 dB,and 20 dB, respectively, varying with the quantity of cognitive userssharing information ρA_(C).

Graph 1400 shows that an arbitrarily low HTP can be achieved in someembodiments by enlisting an adequate number of cognitive users ρA_(C) insharing their sensing information. A target HTP can be achieved byrequiring a threshold number of cognitive user participants. Thisthreshold can be called the critical mass of collective sensing. Thecritical mass M_(C) of collective sensing, cognitive user participantscan be defined as

M_(C)=ρA_(C)   (102)

such that Equation (101) achieves a predefined HTP. Graph 1500 of FIG.15 depicts the M_(C)s required to achieve an HTP of 10⁻⁴ in a shadowingenvironment with σ=11.8 dB for varying values of sensing gainP_(D)/P_(S) and area ratio A_(C)/A₁. For example, when A_(C)/A₁=2 andP_(D)/P_(S)=10 dB, a critical mass of 11 cognitive users can achieve atarget HTP of 10⁻⁴.

Rate Coverage Area:

Supporting system rates as discussed above can require different SNRs. Acoverage area (i.e. cell size) for a particular data rate can be definedas an area around a transmitter where a received signal SNR is above arequired SNR level corresponding to the data rate.

A log-normal path loss model of Equation (88) can be used to calculate acoverage area. A cognitive transmitter can be assumed to have aclearance of 10 meters (noting the same clearance is used in the TV-bandper the NPRM [FCC, May 2004, op. cit.]. Within the clearance distance,signal propagation can follow a free-space propagation model. Signalpower at the edge of a clearance region can be calculated as

$\begin{matrix}{{P\left( r_{0} \right)} = {\left( \frac{c}{4\pi \; {fr}_{0}} \right)^{2}P_{TX}}} & (103)\end{matrix}$

which is also known as the Friis free-space equation [T. S. Rappaport,op. cit.], and where c is the speed of light. Signal power loss from atransmitter to an edge of the clearance region can be expressed

$\begin{matrix}{{K^{dB}\left( r_{0} \right)} = {{P_{TX}^{dB} - {P^{dB}\left( r_{0} \right)}} = {20\; {\log_{10}\left( \frac{c}{4\pi \; {fr}_{0}} \right)}}}} & (104)\end{matrix}$

For example, K(r₀) is 48 dB if r₀=10 m and f=600 MHz. Signal propagationbeyond the clearance region can follow a log-normal path loss model asshown in Equation (88), and signal power at a distance r away from thetransmitter can be expressed

$\begin{matrix}{{P^{dB}(r)} = {P_{TX}^{dB} - {K^{dB}\left( r_{0} \right)} - {\alpha \left( {r^{dB} - r_{0}^{dB}} \right)}}} & (105)\end{matrix}$

noting the distance is expressed in dB units as well. Successfulreception can be achieved for signal power exceeding a threshold V_(T).The coverage area radius can be expressed

$\begin{matrix}{\left( {r^{dB} - r_{0}^{dB}} \right) = {\frac{1}{\alpha}\left\lbrack {P_{TX}^{dB} - {K^{dB}\left( r_{0} \right)} - V_{T}^{dB}} \right\rbrack}} & (106)\end{matrix}$

where a decoding threshold is expressed as

V_(T)^(dB) = N₀^(dB) + NF + SNR_(T)^(dB)

and where N₀=−106 dBm is the thermal noise in a 6 MHz TV channel; NF isthe receiver noise figure; and SNR_(T) is the SNR threshold forsuccessful decoding. SNR thresholds for various data rates are describedpreviously.

Graph 1600 of FIG. 16 shows signal coverage areas at different datarates assuming a carrier frequency of 600 MHz, a clearance radius of 10m, transmission power of 30 dBm (1 W), a path loss exponent α=3.3, and areceiver noise figure of 8 dB. It can be appreciated that these coverageareas can be derived based on additive white gaussian channel (AWGN)SNRs and that areas can shrink if multipath margin is included in theSNRs. Notably, relative coverage area sizes can be independent oftransmission power.

Collective Sensing:

Collective sensing can be key to reducing hidden terminal problems in aTV-band cognitive system as discussed herein. Collective sensing can beemployed in some embodiments as described herein following.

Essentially all cognitive users considered in an embodiment can berequired to periodically, e.g. every 10 seconds, broadcast their sensingresults. A broadcast message from each cognitive user can include itsSNR estimates and/or DTV and/or NTSC signal sensing outcomes on all TVchannels. In some embodiments, such a broadcast message can betransmitted using a lowest data rate that has a largest coverage area,as shown in FIG. 16.

Each cognitive user in an embodiment can also “listen” to (receive andrespond to) messages from potentially all of the other cognitive usersin the embodiment. In order to transmit and/or otherwise use a TVchannel, a cognitive user must collect negative sensing results on thetarget TV channel from at least M_(C) cognitive users where M_(C) is acritical mass corresponding to a specified level of hidden terminalprobability as discussed regards Equation (102). For example, suppose aharmful interference level P_(H) (as shown in FIG. 11) is at the noisefloor. Since sensing results can be shared using the lowest data ratewith a required SNR of −11 dB, an area ratio between the sensing resultsharing region and potential interference region can be expressed asapproximately

A _(C) ^(dB) −A _(C) ^(dB)=2(r _(C) ^(dB) −r ₁ ^(dB))≈2·10/3.3=6 dB  (107)

corresponding to A_(C)/A₁=4 in linear scale. Using sensing methods asdiscussed herein, a sensing gain P_(D)/P_(S) of 15 dB can be achieved.Referring to FIG. 15, a required critical mass for 10⁻⁴ HTP is then 9.

ATSC Signal Sensing: ATSC Frame Format:

An ATSC data transmission can be organized into Data Frames as shown indiagram 1700 of FIG. 17. Each Data Frame contains two Data Fields, eachcontaining 313 Data Segments. The first Data Segment of each Data Fieldis a unique training sequence that can typically be used for channelequalization in a receiver.

The remaining 312 Data Segments carry data. Each Data Segment encodes a188-byte transport stream packet and its associated 20 Reed Solomon FECbytes. Each data segment consists of 832 8-VSB symbols and each symbolis a 8-level signal carrying 3 bits of information. The first 4 symbolsof the Data Segment corresponding to the first (sync) byte of the188-byte transport stream are transmitted in binary form and providesegment synchronization. The remaining 828 symbols can carry a total of828*3=2484 bits of information. Since the symbols are 2/3 trellis coded,the equivalent number of data bits carried in the 828 symbols can be2484*2/3=1656 or 207 data bytes, e.g. 187 transport stream bytes plus 20RS FEC bytes. Notably, the first byte of a 188-byte transport packet istransmitted in the first 4 symbols of a Data Segment for segment sync.

NTSC Signal Sensing:

An NTSC (analog TV) signal can contain narrowband video, chroma, andaudio carriers. These carriers can be at 1.25 MHz, 4.83 MHz, and 5.75MHz from the left band edge, respectively. These relationships areillustrated in diagram 600 of FIG. 6. NTSC signal sensing can be basedon detecting narrowband NTSC video and audio carriers. An FFT-basednarrowband filter scheme can be used in order to detect video and audiocarriers, as illustrated in diagram 2000 of FIG. 20.

A FFT size can be selected so as to be large enough to enable resolutionof video and audio carriers. For example, for a 6 MHz TV channel, an FFTsize of 256 or bigger can be used. As shown in diagram 2000, an inputsignal spectrum can be obtained by transforming a time-domain inputsignal to frequency domain using FFT. Bins around a video carrier can beextracted, and bins around an audio carrier can be extracted. Module FFT2002 can perform an FFT operation on an input signal.

The number of bins taken for detecting the video and/or audio carrierscan be specified, and can depend on channel bandwidth and FFT size. Thenumber of bins taken can be specified to be large enough so that theprobability of missing a target carrier is minimized . A missing carriercan occur if there is frequency offset between transmitter and receiver.For example, for a system that uses a 1024-point FFT over a 6 MHzchannel bandwidth, the subcarrier spacing can be 5.86 kHz. It followsthat in some embodiments of detection, 10 bins each around the video andaudio carriers can be specified. Modules 2010 2020 2030 can be employedto extract video carrier, midband, and audio carrier bins, respectively.

In order to detect either video or audio carriers, power on eachextracted bin can be evaluated, and an average over the bins taken.Magnitude Square modules 2012 2022 2032 can provide amagnitude-squared-based power measurement for video carrier, midband,and audio carrier signals, respectively. The power measurements can bebin-averaged by Averaging (over bins) modules 2014 2024 2034 (againrespectively). The resulting bin-averaged power(s) can each be averagedagain over a predefined number of symbols, e.g. 10, in order to improvesensing reliability. The bin-averaged power measurements can besymbol-averaged by Averaging (over bins) modules 2014 2024 2034 (againrespectively). Averaging processes can use implementations discussedherein and/or any other known and/or convenient averaging technique. Insome embodiments, for averaging over a small number of elements, arunning sum approach can be advantageously employed.

Averaged video and audio carrier powers can be appropriately normalizedbefore thresholding for detection. An average power over bins in thecenter of a TV channel can be used as a basis for normalization, basedon FIG. 6. According to FIG. 6, bins in the center of a TV channel canbe relatively free of narrowband carriers, and, their average power canbe a relatively accurate reflection of average power over the wholechannel. Video and audio carrier powers can be expected to exceedaverage power over mid-band bins by a margin. The margin value can, byway of non-limiting example, be 10 dB. Further, comparing normalizedvideo and audio carriers with such a threshold can enable adetermination of the presence of a NTSC signal. If the video and audiocarriers each exceed such a threshold, a positive NTSC signal detectionresult can apply. If the carriers do not exceed a threshold, a negativeNTSC signal detection result can apply. A threshold 2004 module canperform a comparison between video carrier power and midband power,providing a NTSC signal detection result to an AND 2008 module. Athreshold 2006 module can perform a a comparison between audio carrierpower and midband power, providing a NTSC signal detection result to anAND 2008 module. An AND 2008 module can be employed to logically combinethe result of signal detection results from threshold 2004 and threshold2006 in order to provide a combined NTSC signal detection result. An AND2008 module can perform specified logic and/or other operations (such astime-based operations) on provided input results in order to provide oneor more combined results.

Diagram 2200 of FIG. 22 depicts some embodiments of combined sensingmethods for cognitive radio.

A cognitive radio unit CR 2201 can comprise a phase sensing element2203, an energy sensing element 2205, and a collective sensing element2206. Phase sensing element 2204 and energy sensing element 2205 can beadapted to provide sensing functions as discussed herein, employingphase sensing techniques and energy sensing techniques, respectively.Phase sensing element 2204 and energy sensing element 2005 can beadapted to communicate and/or cooperate with each other, that is,interoperate, according to a specified physical layer 2202. A cognitiveradio unit CR 2201 can further comprise a collective sensing element2202. A collective sensing element 2202 can interoperate with othersensing elements according to a specified network and protocol layer2202. As depicted in Diagram 2200, phase sensing 2204 and/or energysensing 2205 elements can also interoperate with other sensing elementsaccording to a specified network and protocol layer 2202.

As illustrated in Diagram 2200, a second cognitive radio unit CR 221 canbe substantially similar in architecture to CR 2201. CR 221 can comprisea phase sensing element 2214, an energy sensing element 2215, and acollective sensing element 2216. Phase sensing element 2214 and energysensing element 2215 can be adapted to interoperate according to aspecified physical layer 2213. A collective sensing element 2202 caninteroperate with other sensing elements according to a specifiednetwork and protocol layer 2202. Phase sensing 2214 and/or energysensing 2215 elements can also interoperate with other sensing elementsaccording to a specified network and protocol layer 2212.

Sensing elements in a first cognitive radio unit CR1 2201 caninteroperate with sensing elements in a second cognitive radio unit CR22211 according to one or more specified network and protocol layers. Insome embodiments, a specified network and protocol layer 2202 can be thesame network and protocol layer 2212.

Physical layer sensing can comprise phase sensing and/or energy sensing.In some embodiments physical layer sensing can be advantageouslycombined with collective sensing, through utilization of network andprotocol layer(s).

Sensing decisions for individual and/or multiple cognitive radio units2201 221 can be determined collectively through combining physical layersensing results from a plurality of nearby cognitive radio units. Insome embodiments, a collective sensing result can be determined as aweighted average of physical layer sensing results from nearby cognitiveradio units. A weighting for a physical layer sensing result can bebased on a specified and/or measured distance between a cognitive radiounit and another cognitive radio unit and/or a specified location; aspecified location can be the location of a physical layer sensingprocess. That is, in some embodiments a weighting can be responsive tomeasured and/or specified locations and/or distances: between cognitiveradio units, between a cognitive radio unit and a specified location,and/or between specified locations. In some embodiments, a distancemetric can be based on a measurement of radio signal strength detectedfrom a specified cognitive radio unit.

By way of non-limiting examples, communications for collective sensingand between two or more cognitive radio units 2201 2211 can be achievedthrough one or more of a specified cognitive radio channel, a cellularlink, a WiFi link, an Ethernet link, and/or any other known and/orconvenient wired and/or wireless communication systems. By way ofnon-limiting examples, such communications can be adapted for awide-area network (WAN) and/or any other known and/or convenienttechnology for network communications.

Hidden Terminal Probability Derivations: Hidden Terminal Probability:

A large number N_(∞) of cognitive users can be assumed to be randomlydistributed over a large area A_(∞), covering the area of interest A.The probability of no user appearing in A, i.e. all users appearingoutside A, can be expressed:

$\begin{matrix}{{P_{0}(A)} = {\left( {1 - \frac{A}{A_{\infty}}} \right)^{N_{\infty}} = {\left\lbrack \left( {1 - \frac{A}{A_{\infty}}} \right)^{\frac{A}{A}} \right\rbrack^{\frac{N_{\infty}}{A_{\infty}}A} = ^{{- \rho}\; A}}}} & (108)\end{matrix}$

where

$\rho = \frac{N_{\infty}}{A_{\infty}}$

is the cognitive user area density. The probability of one or morecognitive users appearing in A is

P ₁₊(A)=1−P ₀(A)=1−e ^(−ρA)   (109)

The probability of k users appearing in A can be calculated as

$\begin{matrix}\begin{matrix}{{P_{k}(A)} = {\begin{pmatrix}N_{\infty} \\k\end{pmatrix}\left( \frac{A}{A_{\infty}} \right)^{k}\left( {1 - \frac{A}{A_{\infty}}} \right)^{N_{\infty} - k}}} \\{= {\frac{A^{k}}{k!}\underset{\underset{->\rho^{k}}{}}{\left\lbrack {\frac{N_{\infty}}{A_{\infty}}\frac{N_{\infty} - 1}{A_{\infty}}\mspace{11mu} \ldots \mspace{11mu} \frac{N_{\infty} - \left( {k - 1} \right)}{A_{\infty}}} \right\rbrack}}} \\{{\left( {1 - \frac{A}{A_{\infty}}} \right)^{N_{\infty}}\underset{\underset{->1}{}}{\left( {1 - \frac{A}{A_{\infty}}} \right)^{- k}}}} \\{= {\frac{\left( {\rho \; A} \right)^{k}}{k!}^{{- \rho}\; A}}}\end{matrix} & (110)\end{matrix}$

HTP in Shadowing Environment:

The probability that k users appear in an effective interference regionA₁ can be expressed

$\begin{matrix}{\frac{\left( {\rho \; A_{I}} \right)^{k}}{k!}^{{- \rho}\; A_{I}}} & (111)\end{matrix}$

The probability that all k users have successfully sensed the signalfrom U₁ can be expressed as

Γ^(k)   (112)

The probability that at least one user has not successfully sensed thesignal from U₁ can be expressed as

1−Γ^(k)   (113)

This can represent an interference probability, given k users are insidethe interference region A₁. An overall HTP can then be expressed

$\begin{matrix}\begin{matrix}{{\sum\limits_{k = 1}^{\infty}{\left\lbrack {\frac{\left( {\rho \; A_{I}} \right)^{k}}{k!}^{{- \rho}\; A_{I}}} \right\rbrack \left( {1 - \Gamma^{k}} \right)}} = {^{{- \rho}\; A_{I}}\left\lbrack {{\sum\limits_{k = 1}^{\infty}\frac{\left( {\rho \; A_{I}} \right)^{k}}{k!}} - \frac{\left( {\rho \; A_{I}\Gamma} \right)^{k}}{k!}} \right\rbrack}} \\{= {^{{- \rho}\; A_{I}}\left\lbrack {{\sum\limits_{k = 1}^{\infty}\frac{\left( {\rho \; A_{I}} \right)^{k}}{k!}} - \frac{\left( {\rho \; A_{I}\Gamma} \right)^{k}}{k!}} \right\rbrack}} \\{= {^{{- \rho}\; A_{I}}\left\lbrack {^{\rho \; A_{I}} - 1 - \left( {^{\rho \; A_{I}T} - 1} \right)} \right\rbrack}} \\{= {1 - ^{{- \rho}\; {A_{I}{({1 - \Gamma})}}}}}\end{matrix} & (114)\end{matrix}$

HTP with Sensing Information Sharing:

For any k users appearing in A₁, interference can occur when all k usersare not sensing a signal from U₁, in combination with all usersappearing in the area A_(C)-A₁ not sensing a signal from U₁. The totalHTP can be expressed

$\begin{matrix}{{\sum\limits_{k = 1}^{\infty}{\frac{\left( {\rho \; A_{I}} \right)^{k}}{k!}{^{{- \rho}\; A_{l}}\left( {1 - \Gamma} \right)}^{k}\left\{ {\sum\limits_{l = 0}^{\infty}{\frac{\left\lbrack {\rho \left( {A_{C} - A_{I}} \right)} \right\rbrack^{l}}{l!}{^{- {\rho {({A_{C} - A_{I}})}}}\left( {1 - \Gamma} \right)}^{l}}} \right\}}} = {{\sum\limits_{k = 1}^{\infty}{\frac{\left( {\rho \; A_{I}} \right)^{k}}{k!}{^{{- \rho}\; A_{I}}\left( {1 - \Gamma} \right)}^{k}\left\{ {^{- {\rho {({A_{C} - A_{I}})}}}^{{\rho {({A_{C} - A_{I}})}}{({1 - \Gamma})}}} \right\}}} = {{^{{- {\rho {({A_{C} - A_{I}})}}}\Gamma}^{{- \rho}\; A_{I}}{\sum\limits_{k = 1}^{\infty}{\frac{\left( {\rho \; A_{I}} \right)^{k}}{k!}\left( {1 - \Gamma} \right)^{k}}}} = {{^{{- \rho}\; A_{C}\Gamma}{^{{- \rho}\; {A_{I}{({1 - \Gamma})}}}\left\lbrack {^{\rho \; {A_{I}{({1 - \Gamma})}}} - 1} \right\rbrack}} = {^{{- \rho}\; A_{C}\Gamma}\left\lbrack {1 - ^{{- \rho}\; {A_{I}{({1 - \Gamma})}}}} \right\rbrack}}}}} & (115)\end{matrix}$

In the foregoing specification, the embodiments have been described withreference to specific elements thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the embodiments. Forexample, the reader is to understand that the specific ordering andcombination of process actions shown in the process flow diagramsdescribed herein is merely illustrative, and that using different oradditional process actions, or a different combination or ordering ofprocess actions can be used to enact the embodiments. For example,specific reference to NTSC and/or ATSC and/or DTV embodiments areprovided by way of non-limiting examples. Systems and methods hereindescribed can be applicable to any other known and/or convenientchannel-based communication embodiments; these can comprise singleand/or multiple carriers per channel and can comprise a variety ofspecified channel bandwidths. The specification and drawings are,accordingly, to be regarded in an illustrative rather than restrictivesense.

1. A method comprising; receiving a signal; and detecting parametersassociated with said signal.